从缺失的反中学习:示例与基于模型的方法.
Jerker Denrell1, Adam N Sanborn2, Jake Spicer2
1Warwick Business School, University of Warwick.
Journal of experimental psychology. Learning, memory, and cognition
|December 12, 2024
概括
人们通过使用示例或基于模型的学习策略来适应偏见的反. 有些人认为缺失的负面结果,而另一些人则使用贝叶斯模型来纠正选择偏差.
科学领域:
- 认知心理学 认知心理学
- 机器学习是机器学习.
- 决策科学科学 决策科学
背景情况:
- 现实世界的反往往是有选择的,导致偏见的样本有利于积极的结果.
- 决策者面临的挑战是从这种有偏见的数据中学习,并纠正固有的偏见.
研究的目的:
- 研究个人如何从选择性偏见的反中学习.
- 为了确定人们是否可以纠正学习中的选择偏差.
- 为了比较示例和基于模型的学习方法来处理缺失的反.
主要方法:
- 描述了从偏向样本中学习分类的计算问题.
- 检查了示例模型 (归因缺失的负面结果) 和基于模型的方法 (调整任务表示).
- 进行了三项实验,以测试依赖归算与贝叶斯模型对偏差校正的依赖性.
主要成果:
- 参与者采用了多种不同的策略,其中许多最好用示例模型 (通常与归算) 来描述.
- 几乎相同比例的参与者最好用贝叶斯模型来描述.
- 个别策略在任务之间显示了一些稳定性,但受到任务不确定性的影响.
结论:
- 人们通过各种方法来适应处理缺失的反.
- 学习者使用基于示例的归算和基于模型的贝叶斯校正.
- 策略选择受到任务假设和背景的影响,证明了自适应性学习.
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